Fast Sparse Decision Tree Optimization via Reference Ensembles.
Fast Sparse Decision Tree Optimization via Reference Ensembles.
复制标题
DOI：
10.1609/aaai.v36i9.21194
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Seltzer, Margo
中科院分区：
文献类型：
--
作者：
McTavish, Hayden;Zhong, Chudi;Achermann, Reto;Karimalis, Ilias;Chen, Jacques;Rudin, Cynthia;Seltzer, Margo
稀疏的决策树优化是自AI以来最根本的问题之一，在可解释的机器学习的核心方面是一个挑战。该问题仅在过去几年内，主要是关于找到最佳稀疏决策树的问题。鉴于这些决策树优化问题的搜索空间是巨大的，尤其是那些具有多个连续价值的功能的数据集，我们是否真的希望找到一个稀疏的决策树，以与黑匣子机器学习模型准确竞争？我们通过智能猜测策略来解决这个问题，这些策略可以应用于任何基于分支机构的决策树算法。通过多个数量级的时间，同时提供了界限的范围，而所产生的树可以偏离黑匣子的准确性和表达能力。最佳决策树。我们的实验表明，在许多情况下，我们可以迅速构建与黑匣子模型的准确性相匹配的稀疏决策树。
Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, and despite steady effort since the 1960’s, breakthroughs have been made on the problem only within the past few years, primarily on the problem of finding optimal sparse decision trees. However, current state-of-the-art algorithms often require impractical amounts of computation time and memory to find optimal or near-optimal trees for some real-world datasets, particularly those having several continuous-valued features. Given that the search spaces of these decision tree optimization problems are massive, can we practically hope to find a sparse decision tree that competes in accuracy with a black box machine learning model? We address this problem via smart guessing strategies that can be applied to any optimal branch-and-bound-based decision tree algorithm. The guesses come from knowledge gleaned from black box models. We show that by using these guesses, we can reduce the run time by multiple orders of magnitude while providing bounds on how far the resulting trees can deviate from the black box’s accuracy and expressive power. Our approach enables guesses about how to bin continuous features, the size of the tree, and lower bounds on the error for the optimal decision tree. Our experiments show that in many cases we can rapidly construct sparse decision trees that match the accuracy of black box models. To summarize: when you are having trouble optimizing, just guess.
登录
查看更多内容
影响因子：
7.5
作者：
Bertsimas, Dimitris;Dunn, Jack
通讯作者：
Dunn, Jack
影响因子：
3.7
作者：
MORGAN, JN;SONQUIST, JA
通讯作者：
SONQUIST, JA
影响因子：
1.8
作者：
Gunluk, Oktay;Kalagnanam, Jayant;Scheinberg, Katya
通讯作者：
Scheinberg, Katya
影响因子：
4.5
作者：
Friedman, JH
通讯作者：
Friedman, JH
影响因子：
1.7
作者：
Carrizosa E;Molero-Río C;Romero Morales D
通讯作者：
Romero Morales D